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      High seroprevalence of SARS-CoV-2 in Burkina-Faso, Ghana and Madagascar in 2021: a population-based study

      research-article
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      BMC Public Health
      BioMed Central
      SARS-CoV-2, Seroprevalence, Population-based, Sub-Saharan Africa, Bayesian model

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          Abstract

          Background

          The current COVID-19 pandemic affects the entire world population and has serious health, economic and social consequences. Assessing the prevalence of COVID-19 through population-based serological surveys is essential to monitor the progression of the epidemic, especially in African countries where the extent of SARS-CoV-2 spread remains unclear.

          Methods

          A two-stage cluster population-based SARS-CoV-2 seroprevalence survey was conducted in Bobo-Dioulasso and in Ouagadougou, Burkina Faso, Fianarantsoa, Madagascar and Kumasi, Ghana between February and June 2021. IgG seropositivity was determined in 2,163 households with a specificity improved SARS-CoV-2 Enzyme-linked Immunosorbent Assay. Population seroprevalence was evaluated using a Bayesian logistic regression model that accounted for test performance and age, sex and neighbourhood of the participants.

          Results

          Seroprevalence adjusted for test performance and population characteristics were 55.7% [95% Credible Interval (CrI) 49·0; 62·8] in Bobo-Dioulasso, 37·4% [95% CrI 31·3; 43·5] in Ouagadougou, 41·5% [95% CrI 36·5; 47·2] in Fianarantsoa, and 41·2% [95% CrI 34·5; 49·0] in Kumasi. Within the study population, less than 6% of participants performed a test for acute SARS-CoV-2 infection since the onset of the pandemic.

          Conclusions

          High exposure to SARS-CoV-2 was found in the surveyed regions albeit below the herd immunity threshold and with a low rate of previous testing for acute infections. Despite the high seroprevalence in our study population, the duration of protection from naturally acquired immunity remains unclear and new virus variants continue to emerge. This highlights the importance of vaccine deployment and continued preventive measures to protect the population at risk.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12889-022-13918-y.

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          Most cited references28

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          Who is wearing a mask? Gender-, age-, and location-related differences during the COVID-19 pandemic

          Masks are an effective tool in combatting the spread of COVID-19, but some people still resist wearing them and mask-wearing behavior has not been experimentally studied in the United States. To understand the demographics of mask wearers and resistors, and the impact of mandates on mask-wearing behavior, we observed shoppers (n = 9935) entering retail stores during periods of June, July, and August 2020. Approximately 41% of the June sample wore a mask. At that time, the odds of an individual wearing a mask increased significantly with age and was also 1.5x greater for females than males. Additionally, the odds of observing a mask on an urban or suburban shopper were ~4x that for rural areas. Mask mandates enacted in late July and August increased mask-wearing compliance to over 90% in all groups, but a small percentage of resistors remained. Thus, gender, age, and location factor into whether shoppers in the United States wear a mask or face covering voluntarily. Additionally, mask mandates are necessary to increase mask wearing among the public to a level required to mitigate the spread of COVID-19.
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            Serological evidence of human infection with SARS-CoV-2: a systematic review and meta-analysis

            Background A rapidly increasing number of serological surveys for antibodies to SARS-CoV-2 have been reported worldwide. We aimed to synthesise, combine, and assess this large corpus of data. Methods In this systematic review and meta-analysis, we searched PubMed, Embase, Web of Science, and five preprint servers for articles published in English between Dec 1, 2019, and Dec 22, 2020. Studies evaluating SARS-CoV-2 seroprevalence in humans after the first identified case in the area were included. Studies that only reported serological responses among patients with COVID-19, those using known infection status samples, or any animal experiments were all excluded. All data used for analysis were extracted from included papers. Study quality was assessed using a standardised scale. We estimated age-specific, sex-specific, and race-specific seroprevalence by WHO regions and subpopulations with different levels of exposures, and the ratio of serology-identified infections to virologically confirmed cases. This study is registered with PROSPERO, CRD42020198253. Findings 16 506 studies were identified in the initial search, 2523 were assessed for eligibility after removal of duplicates and inappropriate titles and abstracts, and 404 serological studies (representing tests in 5 168 360 individuals) were included in the meta-analysis. In the 82 studies of higher quality, close contacts (18·0%, 95% CI 15·7–20·3) and high-risk health-care workers (17·1%, 9·9–24·4) had higher seroprevalence than did low-risk health-care workers (4·2%, 1·5–6·9) and the general population (8·0%, 6·8–9·2). The heterogeneity between included studies was high, with an overall I 2 of 99·9% (p<0·0001). Seroprevalence varied greatly across WHO regions, with the lowest seroprevalence of general populations in the Western Pacific region (1·7%, 95% CI 0·0–5·0). The pooled infection-to-case ratio was similar between the region of the Americas (6·9, 95% CI 2·7–17·3) and the European region (8·4, 6·5–10·7), but higher in India (56·5, 28·5–112·0), the only country in the South-East Asia region with data. Interpretation Antibody-mediated herd immunity is far from being reached in most settings. Estimates of the ratio of serologically detected infections per virologically confirmed cases across WHO regions can help provide insights into the true proportion of the population infected from routine confirmation data. Funding National Science Fund for Distinguished Young Scholars, Key Emergency Project of Shanghai Science and Technology Committee, Program of Shanghai Academic/Technology Research Leader, National Science and Technology Major project of China, the US National Institutes of Health. Translation For the Chinese translation of the abstract see Supplementary Materials section.
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              SeroTracker: a global SARS-CoV-2 seroprevalence dashboard

              As the initial phase of the COVID-19 pandemic passes its peak in many countries, serological studies are becoming increasingly important in guiding public health responses. Antibody testing is crucial for monitoring the evolution of the pandemic, providing a more complete picture of the total number of people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) than molecular diagnostic testing alone. 1 All individuals with SARS-CoV-2-specific antibodies have been exposed to the virus, so antibody testing can highlight differences in past exposure between regions, demographic groups, and occupations. 2 Seroprevalence estimates can also be used to estimate the infection fatality rate. 3 Dashboards that visualise COVID-19 cases confirmed by diagnostic testing have been pivotal in enabling policy makers and researchers to monitor the pandemic. 4 Yet, despite the value of antibody testing, there is no unified resource for seroprevalence estimates. To address this need, we created SeroTracker, a custom-built dashboard that systematically monitors and synthesises findings from hundreds of global SARS-CoV-2 serological studies. The dashboard allows users to visualise seroprevalence estimates on a world map and compare estimates between regions, population groups, and testing modalities (eg, assay type or antibody isotype). SeroTracker integrates evidence from serosurveillance studies through a live systematic review. 5 Each day, published articles (MEDLINE, Embase, Web of Science, and Cochrane), preprints (medRxiv and bioRxiv), government reports, and news articles are reviewed for newly reported SARS-CoV-2 seroprevalence estimates by a team of doctoral and medical students. Over 13 000 records have been screened to date. Seroprevalence estimates are extracted from each article, in addition to the sample size, sampling approach, study population, and antibody test used. Risk of bias for each prevalence estimate is assessed using the Joanna Briggs Institute Critical Appraisal Guidelines for Prevalence studies. 6 As of July 23, 2020, 162 studies are being monitored, with data available through the SeroTracker website and dashboard code accessible through GitHub. SeroTracker presents data on an Explore map tab and an Analyze chart tab. The Explore tab features a world map where countries are coloured by seroprevalence. Hovering over each country displays the aggregated seroprevalence estimate along with a 95% CI, the total number of seroprevalence estimates available for that country, and the number of antibody tests administered. This birds-eye view shows that most reported seroprevalence estimates are in the USA and Europe (appendix). The Analyze tab offers a more granular view, providing seroprevalence estimates stratified by geography, age, or population group, among other variables. This tab also features a references table that summarises the sources from which these estimates were extracted. Across both tabs, users can also filter data by geography, study characteristics (source type, study status, overall risk of bias), population demographics (age, sex, general population, health-care workers), and test information (test type, reported isotypes). Filtering for national or regional seroprevalence estimates in the general population yields studies in 12 countries. Comparing these estimates to diagnostic testing data suggests that SARS-CoV-2 has infected many more individuals than case counts indicate (appendix). SeroTracker has proven useful to researchers, policy makers, and public health officials. For example, one group is using our data to estimate COVID-19 infection fatality rates globally. Recognising the limitations of serological tests and serosurvey study designs, our dashboard shows by default only studies at low and moderate risk of bias, and we plan to adjust seroprevalence estimates on the basis of test sensitivity and specificity. Further features will allow visualisation of how seroprevalence is changing over time, overlay diagnostic testing data alongside seroprevalence estimates, and show seroprevalence estimates in specific states and provinces. We will continue to host SeroTracker throughout the COVID-19 outbreak to support evidence-based decision making.
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                Author and article information

                Contributors
                struck@bnitm.de
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                5 September 2022
                5 September 2022
                2022
                : 22
                : 1676
                Affiliations
                [1 ]Infectious Disease Epidemiology, Bernhard Nocht Insitute for Tropical Medicine, Hamburg, Germany
                [2 ]GRID grid.452463.2, German Center for Infection Research (DZIF), , Hamburg-Borstel-Lübeck-Riems, ; Heidelberg, Germany
                [3 ]GRID grid.5802.f, ISNI 0000 0001 1941 7111, Institute of Medical Biostatistics, Epidemiology and Informatics, , University Medical Centre of the Johannes Gutenberg, University Mainz, ; Mainz, Germany
                [4 ]GRID grid.424065.1, ISNI 0000 0001 0701 3136, Diagnostics Development Laboratory, , Bernhard Nocht Institute for Tropical Medicine, ; Hamburg, Germany
                [5 ]GRID grid.12082.39, ISNI 0000 0004 1936 7590, University of Sussex Business School, University of Sussex, ; Falmer, UK
                [6 ]GRID grid.9829.a, ISNI 0000000109466120, Kumasi Centre for Collaborative Research in Tropical Medicine, Kwame Nkrumah University of Science and Technology, ; Kumasi, Ghana
                [7 ]GRID grid.9829.a, ISNI 0000000109466120, Department of Molecular Medicine, , Kwame Nkrumah University of Science and Technology, ; Kumasi, Ghana
                [8 ]GRID grid.9829.a, ISNI 0000000109466120, Department of Clinical Microbiology, , Kwame Nkrumah University of Science and Technology, ; Kumasi, Ghana
                [9 ]GRID grid.8652.9, ISNI 0000 0004 1937 1485, Department of Community Health, , University of Ghana, ; Accra, Ghana
                [10 ]GRID grid.450607.0, ISNI 0000 0004 0566 034X, Centre de Recherche en Santé de Nouna, ; Nouna, Burkina Faso
                [11 ]Centre d’Infectiologie Charles Méreiux, Antananarivo, Madagascar
                [12 ]University of Fianarantsoa, Fianarantsoa, Madagascar
                [13 ]GRID grid.440419.c, ISNI 0000 0001 2165 5629, University of Antananarivo, ; Antananarivo, Madagascar
                [14 ]GRID grid.5253.1, ISNI 0000 0001 0328 4908, Heidelberg Institute of Global Health (HIGH), Heidelberg University Hospital, Heidelberg University, ; Heidelberg, Germany
                [15 ]GRID grid.9829.a, ISNI 0000000109466120, Department of Global and International Health, , Kwame Nkrumah University of Science and Technology, ; Kumasi, Ghana
                [16 ]GRID grid.13648.38, ISNI 0000 0001 2180 3484, Department of Tropical Medicine I, , University Medical Center Hamburg-Eppendorf (UKE), ; Hamburg, Germany
                Article
                13918
                10.1186/s12889-022-13918-y
                9441841
                36064368
                0919baa5-4dfe-4b57-af55-148f9798bed5
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 30 March 2022
                : 7 July 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003107, Bundesministerium für Gesundheit;
                Award ID: ZMVI1-2520COR001
                Award ID: ZMVI1-2520COR001
                Award ID: ZMVI1-2520COR001
                Award ID: ZMVI1-2520COR001
                Award ID: ZMVI1-2520COR001
                Award Recipient :
                Funded by: Bernhard-Nocht-Institut für Tropenmedizin (3424)
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Public health
                sars-cov-2,seroprevalence,population-based,sub-saharan africa,bayesian model
                Public health
                sars-cov-2, seroprevalence, population-based, sub-saharan africa, bayesian model

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